Assignment#8

The “bad” plot

The bad plot I decided to fix is the Spotify chart presenting numbers of artists generating particular amounts of money. I suggested two ways to fix that chart, so there will be two possible approaches.

Creating the dataset

I created the dataset to work on, from the data visible on the chart. Containing year of measurement, artists’ income and number of artists for each income threshold.

library(tidyr)
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(ggplot2)
artist_income <- tibble(
 income = c('$10K+', '$100K+', '$1M+'),
 `2017` = c(23400, 4300, 460),
 `2023` = c(66000, 11600, 1250),
) %>%
  pivot_longer(
    cols = c(`2017`, `2023`),
    names_to = 'year',
    values_to = 'num_of_artists'
) %>%
  mutate(
    year = factor(year, levels = c('2017', '2023')),
    income = factor(income, levels = c('$10K+', '$100K+', "$1OK+", '$1M+'))
  )
artist_income

Three separated bar charts

Rather than combining all three charts onto one axis, I create three small to ensure the axis and values are correct. Each barplot has its own axis to better reflect changes throughout the years.

three_separate_charts <- artist_income %>%
  ggplot(aes(x = year, y = num_of_artists, fill = year)) +
  geom_col(width = 0.6) +
  facet_wrap(~ income, scales = 'free_y') +
  geom_text(aes(label = scales::comma(num_of_artists)), vjust = -0.5, size = 3) +
  scale_fill_manual(values = c('2017' = 'aquamarine2', '2023' = 'aquamarine4')) +
  labs(
    x = NULL,
    y = 'Number of Artists',
    fill = 'Year',
    title = 'The number of artists generating at least $1M, $100K, 10K',
    subtitle = 'It has nearly tripled since 2017 till 2023'
  ) +
  theme_minimal(base_size = 13) +
  theme(
  legend.position = 'top',
  axis.line = element_line(color = 'black'),
  axis.ticks = element_line(color = 'black')
  )
three_separate_charts

Combined bar chart

The second approach is combining all three bar charts into one chart. This method is better to show differences between numbers of artists having particular incomes rather than the difference between their income in 2017 and 2023.

combined_bar_chart <- artist_income %>%
  ggplot(aes(x = income, y = num_of_artists, fill = year)) +
  geom_col(position = position_dodge(width = 0.7), width = 0.6) +
  geom_text(
    aes(label = scales::comma(num_of_artists)),
    position = position_dodge(width = 0.7),
    vjust = -0.5, size = 3.2
  ) +
  scale_fill_manual(values = c('2017' = 'aquamarine', '2023' = 'aquamarine3')) +
  scale_y_continuous(labels = scales::comma, expand = expansion(mult = c(0, 0.25))) +
  labs(
    x = 'Income',
    y = 'Number of Artists',
    fill = 'Year',
    title = 'The number of artists generating at least $1M, $100K, 10K',
    subtitle = 'It has nearly tripled since 2017 till 2023'
  ) +
  theme_minimal(base_size = 12) +
  theme(
    legend.position = 'top',
    axis.line = element_line(color = 'black'),
    axis.ticks = element_line(color = 'black')
  )
combined_bar_chart

Plotly implementation

• First chart

  library(plotly)

  
  Attaching package: 'plotly'

  The following object is masked from 'package:ggplot2':
  
      last_plot

  The following object is masked from 'package:stats':
  
      filter

  The following object is masked from 'package:graphics':
  
      layout

  ggplotly(three_separate_charts)

• Second chart

  ggplotly(combined_bar_chart)